Libraries and settings

1. Import and format carbon chemestry data

Notes: * duplicated labels LK and 21LK; WS and WS cruise

Add seasonality

Winter: Dec 20-March 19
Spring: March 20-June 18
Summer: June 19-Sep 20
Fall: Sep 21-Dec 19
Wet: May - October
Dry: November - April

Subset only FK data

This removes samples from Gulf of Mexico
Add reef zone: “Inshore”, “Mid channel”, “Offshore”, “Oceanic”
Add reef subregion: “BB”, “UK”, “MK”, “LK”

 #Get only FLK data#
  Metadata<-read.csv("Data/FLKLocations.csv", header = T)
  
  FLK.data<-join(CC.data, Metadata, 
                 type = "left", by="SiteID")
  str(FLK.data)

## 'data.frame':    1977 obs. of  84 variables:
##  $ CTDID                  : Factor w/ 1978 levels "","WS10067_1",..: 2 13 19 20 21 22 23 24 25 3 ...
##  $ Region                 : Factor w/ 1 level "FL": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Year                   : num  2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ...
##  $ Mission                : Factor w/ 1 level "WaltonSmith": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Location               : Factor w/ 5 levels "FLK","Gulf_of_Mexico",..: 5 5 5 5 5 5 5 4 4 4 ...
##  $ Latitude               : num  25.6 25.6 25.1 25.1 25.1 ...
##  $ Longitude              : num  -80.1 -80.1 -80.4 -80.4 -80.3 ...
##  $ UTCDate                : Date, format: "2010-03-08" "2010-03-08" ...
##  $ UTCTime                : Factor w/ 1099 levels "","0:00:00","0:01:00",..: 208 241 440 460 462 491 517 715 731 749 ...
##  $ Sample_Depth_m         : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ DIC_umol_kg            : num  2073 2068 2187 2112 2081 ...
##  $ TA_umol_kg             : num  2424 2418 2558 2446 2428 ...
##  $ pH_measured            : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ pH_calculated          : num  8.16 8.14 8.19 8.15 8.15 ...
##  $ pCO2_uatm              : num  302 317 293 313 314 ...
##  $ Aragonite_Sat_W        : num  3.83 3.83 4.07 3.62 3.79 ...
##  $ Salinity_Bottle        : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ Conductivity_Sm        : logi  NA NA NA NA NA NA ...
##  $ Salinity_CTD           : num  36.5 36.5 36.7 36.7 36.6 ...
##  $ Temperature_C          : num  20.4 21.6 18.8 18.8 20.8 ...
##  $ Pressure_db            : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Density_Sigmat         : logi  NA NA NA NA NA NA ...
##  $ SiteID                 : Factor w/ 112 levels "","1","10","11",..: 2 29 37 46 47 68 69 80 83 85 ...
##  $ Survey_design          : Factor w/ 1 level "Permanent": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Sample_frequency       : Factor w/ 1 level "Single": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ncrmp_flag             : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ bottle_id              : int  10021 10022 10023 10024 10025 10026 10027 10028 10029 10030 ...
##  $ source_code            : Factor w/ 2 levels "E","O": 1 1 1 1 1 1 1 1 1 1 ...
##  $ sample_num             : int  1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 ...
##  $ bottle_name            : Factor w/ 771 levels "1","10","10A",..: 1 332 495 535 575 615 655 688 718 2 ...
##  $ deployment_id          : int  517 517 517 517 517 517 517 517 517 517 ...
##  $ project_code           : Factor w/ 27 levels "Walton Smith",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ collection_id          : int  9221 9222 9223 9224 9225 9226 9227 9228 9229 9230 ...
##  $ collected_date         : Factor w/ 167 levels "","1/28/2019",..: 81 81 81 81 81 81 81 81 81 81 ...
##  $ collected_time         : Factor w/ 1099 levels "","0:00:00","0:01:00",..: 208 241 440 460 462 491 517 715 731 749 ...
##  $ collected_timezone     : Factor w/ 2 levels "","+00:00": 2 2 2 2 2 2 2 2 2 2 ...
##  $ density_id             : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ dic_id                 : int  17166 17167 17168 17169 17170 17171 17172 17173 17174 17175 ...
##  $ calc_avg_dic           : num  2086 2081 2200 2125 2094 ...
##  $ dic_temp               : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ dic_machine            : Factor w/ 4 levels "","Apollo","SOMMA",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ dic_kg_flag            : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ calibration_dic_id     : int  590 590 590 590 590 590 590 590 590 590 ...
##  $ calc_avg_crm_init_dic  : num  2014 2014 2014 2014 2014 ...
##  $ crm_init_dic_temp      : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ calibration_dic_kg_flag: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ dic_crm_id             : int  16 16 16 16 16 16 16 16 16 16 ...
##  $ dic_crm_batch          : int  85 85 85 85 85 85 85 85 85 85 ...
##  $ dic_crm_dic            : num  2000 2000 2000 2000 2000 ...
##  $ dic_crm_salinity       : num  33.3 33.3 33.3 33.3 33.3 ...
##  $ ta_id                  : int  9096 9097 9098 9099 9100 9101 9102 9103 9104 9105 ...
##  $ calc_avg_ta            : num  2455 2450 2590 2477 2459 ...
##  $ calc_avg_ta_temp       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ ta_machine             : Factor w/ 7 levels "","Apollo","Apollo1",..: 7 7 7 7 7 7 7 7 7 7 ...
##  $ ta_kg_flag             : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ calibration_ta_id      : int  626 626 626 626 626 626 626 626 626 626 ...
##  $ calc_avg_crm_ta        : num  2215 2215 2215 2215 2215 ...
##  $ calc_avg_crm_ta_temp   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ calibration_ta_kg_flag : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ ta_crm_id              : int  16 16 16 16 16 16 16 16 16 16 ...
##  $ ta_crm_batch           : int  85 85 85 85 85 85 85 85 85 85 ...
##  $ ta_crm_ta              : num  2184 2184 2184 2184 2184 ...
##  $ ta_crm_salinity        : num  33.3 33.3 33.3 33.3 33.3 ...
##  $ spec_ph_id             : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ spec_ph_temp           : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ rm_factor              : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ seacarb_id             : int  4570 4571 4572 4573 4574 4575 4576 4577 4578 4579 ...
##  $ UTCDate_Time           : POSIXct, format: "2010-03-08 12:26:00" "2010-03-08 13:05:00" ...
##  $ datetime               : POSIXct, format: "2010-03-08 07:26:00" "2010-03-08 08:05:00" ...
##  $ ESTDate                : Date, format: "2010-03-08" "2010-03-08" ...
##  $ ESTTime                : chr  "07:26:00" "08:05:00" "12:17:00" "12:42:00" ...
##  $ Month                  : Factor w/ 12 levels "Apr","May","Jun",..: 12 12 12 12 12 12 12 12 12 12 ...
##  $ Months                 : Factor w/ 12 levels "January","February",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ MY                     : Factor w/ 54 levels "2010-03","2010-06",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ MoY                    : num  3 3 3 3 3 3 3 3 3 3 ...
##  $ ToD                    : num  7.43 8.08 12.28 12.7 12.75 ...
##  $ Season                 : Factor w/ 4 levels "Fall","Spring",..: 4 4 4 4 4 4 4 4 4 4 ...
##  $ Precipitation          : Factor w/ 2 levels "Dry","Wet": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dec.lon                : num  -80.1 -80.1 -80.4 -80.4 -80.3 ...
##  $ dec.lat                : num  25.6 25.6 25.1 25.1 25.1 ...
##  $ Zone                   : Factor w/ 4 levels "Inshore","Mid channel",..: 1 4 1 1 2 4 3 1 2 4 ...
##  $ Sub_region             : Factor w/ 4 levels "BB","LK","MK",..: 1 1 4 4 4 4 4 3 3 3 ...
##  $ Reference              : Factor w/ 11 levels "BB_1","BB_2",..: 1 1 10 10 10 10 10 7 7 7 ...
##  $ Transect               : Factor w/ 4 levels "","T1","T2","T3": 2 2 2 2 2 2 2 2 2 2 ...

  FLK.data<-subset(FLK.data, FLK.data$Sub_region!="NA")
  
  FLK.data$Sub_region<-factor(FLK.data$Sub_region, levels = c(
                        "BB", "UK", "MK", "LK"))

  FLK.data$Zone<-factor(FLK.data$Zone, levels = c(
                        "Inshore", "Mid channel", 
                        "Offshore", "Oceanic"))

Label known extreme events to check outliers

# Label and filter extreme events
  FLK.data$Extreme<-"Normal"
  FLK.data$Extreme[FLK.data$MY=="2010-03"] <-"ColdMortality"
  FLK.data$Extreme[FLK.data$MY=="2010-08"] <-"Waves/Overcast"
  FLK.data$Extreme[FLK.data$MY=="2011-10"] <-"LowSalinty"
  FLK.data$Extreme[FLK.data$MY=="2011-08"] <-"HighOmega_NoReason"
  FLK.data$Extreme<-as.factor(FLK.data$Extreme)

2. Check data available

2.1 Locations (all samples)

Figure: Map of the sites where individual samples were collected

Notes: * Check location of individual SiteIDs

2.2 Average GPS point location

# Coordinates<-select(FLK.data, "CTDID", "Latitude", "Longitude", "SiteID")
# Coordinates<-join(Coordinates, WS.GPS.Sites,
#                  type = "left", by="SiteID")
# Coordinates$Lat_diff<-abs(Coordinates$Latitude-Coordinates$Lat)
# Coordinates$Lon_diff<-abs(Coordinates$Longitude-Coordinates$Lon)
# Check.coordinates1<- Coordinates[(Coordinates$Lat_diff>0.02), ]
# Check.coordinates2<- Coordinates[(Coordinates$Lon_diff>0.02), ]
# Check.coordinates<- rbind(Check.coordinates1, Check.coordinates2)
# Check.coordinates<-unique(Check.coordinates)
#write.csv(Check.coordinates, "FLK_results/2_CheckGPS_points_v3.csv")

Figure: Permanent location of WS stations

2.3 Number of samples

By zone and location

Sub_region	Inshore	Mid channel	Offshore	Oceanic
BB	84	76	83
UK	107	71	111	37
MK	129	129	130	83
LK	138	138	171	87

By year and sub_region

Samples collected each year
Sub_region	2010	2011	2012	2014	2015	2016	2017	2018	2019	2020	2021
BB	4	14	3	6	30	36	30	35	24	24	37
UK	7	19	3	8	45	51	42	45	30	31	45
MK	15	51	9	11	55	66	55	56	44	44	65
LK	18	61	9	11	58	72	60	76	53	46	70

By climatological season and region

Samples collected in each seson
Sub_region	Fall	Spring	Summer	Winter
LK	149	133	129	123
MK	133	120	121	97
UK	88	82	84	72
BB	68	60	63	52

By Month-year

Samples collected by year-month
2010	2011	2012	2014	2015	2016	2017	2018	2019	2020	2021	Months
					38	38	19	6	35		January
	16	22								38	February
24		2			38	35	33				March
	24			38			16	35		37	April
					38		16			28	May
20	24			38		38	35			8	June
				37	38	18		36		25	July
	28					20	38		35	10	August
				38	38			36			September
	28					38	18		38	36	October
				37	35			38			November
	25		36				37		37	35	December

By Month-region

Samples collected by month-location
Sub_region	Zone	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	Jan	Feb	Mar
BB	Inshore	9	4	8	10	5	6	8	6	10	7	4	7
BB	Mid channel	9	4	5	10	5	6	7	6	8	7	3	6
BB	Offshore	9	4	8	10	5	6	8	6	9	7	4	7
UK	Inshore	13	6	8	13	8	7	9	9	11	10	4	9
UK	Mid channel	6	3	9	9	3	6	6	5	8	7	2	7
UK	Offshore	11	5	12	14	6	9	11	8	11	12	3	9
UK	Oceanic	3	1	5	4	2	3	4	2	4	3	2	4
MK	Inshore	12	8	15	13	11	9	13	9	14	10	6	9
MK	Mid channel	12	8	13	13	11	9	13	9	14	9	7	11
MK	Offshore	13	7	13	13	11	9	13	9	15	10	6	11
MK	Oceanic	7	4	8	8	8	6	8	6	10	6	5	7
LK	Inshore	12	7	16	9	15	9	15	9	15	11	8	12
LK	Mid channel	12	7	15	10	13	9	15	9	15	12	9	12
LK	Offshore	14	10	18	13	18	12	19	12	17	18	7	13
LK	Oceanic	8	4	10	5	10	6	9	5	9	7	6	8

Per station during all sampling time

Samples collected in SiteIDs
SiteID	2010	2011	2012	2014	2015	2016	2017	2018	2019	2020	2021
1	2	6	1	1	5	6	5	6	4	4	6
10	2	5	1	1	5	6	5	5	4	4	5
11	2	4	1	1	5	6	5	5	4	4	6
12	1	5	1	1	5	6	5	6	4	4	6
13	1	4	1	1	5	6	5	4	4	4	6
14	1	3	1	1	5	6	5	5	4	4	6
16	2	6	1	1	5	6	5	6	4	4	5
17	2	6	1	1	5	6	5	7	4	4	5
18	2	6	1	1	5	6	5	7	4	4	6
19	2	6		1	5	6	5	6	4	4	6
20	2	5	1	1	5	6	5	6	4	4	6
21.5	2	6	1	1	4	6	5	5	4	3	6
21LK	1	5		1	5	6	5	5	4	4	6
22	1	3	1	1	5	6	5	7	5	4	6
22.5	1	6	1	1	4	6	5	6	5	3	6
23	1	6	1	1	5	6	5	7	5	4	6
24	2	6	1	1	5	6	5	7	5	4	6
3	2	6	1	1	5	6	5	6	4	4	6
4	1	4		1	5	6	5	4	2	3	4
5	1	4	1	1	5	6	5	4	2	3	4
5.5	2	2	1	1	5	5	4	4	3	3	4
6	2	4		1	5	5	4	4	3	3	5
6.5	1	5	1	1	5	5	4	4	3	3	5
7	2	6		1	5	6	5	6	4	4	6
8	2	5	1	1	5	6	5	6	4	4	6
9	2	6		1	5	6	5	6	4	4	6
9.5	2	6	1	1	5	6	5	4	4	4	6
15		4	1	1	5	6	5	5	4	4	6
15.5		3	1	1	5	6	5	4	4	4	6
2		2	1	1	5	6	5	6	4	4	6
EK_IN				1	5	6	5	6	4	4	7
EK_MID				1	5	6	5	5	4	4	6
EK_OFF				1	5	6	5	6	4	4	6
UK_IN				1	5	6	5	6	4	4	5
UK_MID				1	5	6	5	6	4	4	6
UK_OFF				1	5	6	5	6	4	4	6
MR					5	6	5	7	5	4	6
WS					5	6	5	7	5	4	6

3. TA and DIC salinity normalization

Salinity standarizations

Use salinity Bottle first, if not available, salinity CTD
Calculate TA intercepts

# TA_coef <- FLK.data %>% 
#                 group_by(Region) %>%
#                 do({model = lm(TA_umol_kg~BestSalinity, data=.)  # model
#                     data.frame(tidy(model), # get coefficient info
#                     glance(model))})        # get model info
    
 TA_coef <- FLK.data[FLK.data$Zone=="Oceanic",] %>% 
                 group_by(Region) %>%
                 do({model = lm(TA_umol_kg~BestSalinity, data=.)  # model
                     data.frame(tidy(model), # get coefficient info
                     glance(model))})        # get model info

Calculate DIC intercepts

    DIC_coef <- FLK.data[FLK.data$Zone=="Oceanic",] %>% 
                  group_by(Region) %>%
                  do({model = lm(DIC_umol_kg~BestSalinity, data=.)  
                  # create model
                  data.frame(tidy(model), # get coefficient info
                  glance(model))})        # get model info

    # DIC_coef <- FLK.data %>% 
    #               group_by(Region) %>%
    #               do({model = lm(DIC_umol_kg~BestSalinity, data=.)  
    #               # create model
    #               data.frame(tidy(model), # get coefficient info
    #               glance(model))})        # get model info

Normalize with Friss equation

# 4. Normalize the data
  TA_TO<-subset(TA_coef, term=="(Intercept)")
  TA_TO<-as.data.frame(select(TA_TO, c(Region, estimate)))
  names(TA_TO)[2] <- "TA_0"
  
  DIC_TO<-subset(DIC_coef, term=="(Intercept)")
  DIC_TO<-as.data.frame(select(DIC_TO, c(Region, estimate)))
  names(DIC_TO)[2] <- "DIC_0"
    
  Intercepts<-join(TA_TO, DIC_TO, by="Region", type="full")
  FLK.data<-join(FLK.data, Intercepts, by="Region", type="left")

FLK.data$nTA<-(
    (FLK.data$TA_umol_kg-FLK.data$TA_0)/
    FLK.data$BestSalinity)*MeanSalinity+FLK.data$TA_0

  FLK.data$nDIC<-(
    (FLK.data$DIC_umol_kg-FLK.data$DIC_0)/
    FLK.data$BestSalinity)*MeanSalinity+FLK.data$DIC_0
  
#write.csv(FLK.data, "FLK_results/FLK.data_normalized.csv", row.names = F)

TA parameters to nTA

kable(as.data.frame(TA_coef[,1:8], format = "markdown"), 
      caption = "TA parameters for normalization", digits = 3)

TA parameters for normalization
Region	term	estimate	std.error	statistic	p.value	r.squared	adj.r.squared
FL	(Intercept)	2111.560	57.288	36.859	0	0.099	0.094
FL	BestSalinity	7.515	1.592	4.719	0	0.099	0.094

#write.csv(TA_coef, "TA_coef.csv")

# 5. Chech normalization?
#nTA_Sal<- ggplot(FLK.data[FLK.data$Zone=="Oceanic",] ) + theme_bw() +
  
nTA_Sal<- ggplot(FLK.data) + MyTheme+
facet_grid(~Zone)+
 
  geom_point(aes (BestSalinity, TA_umol_kg, fill=Season), alpha=0.5, shape=21)+
  geom_smooth(aes(BestSalinity, TA_umol_kg), linetype=2, method = "lm", color="black")+
    
  geom_point(aes (BestSalinity, nTA, fill=Season), alpha=0.5, shape=24)+
  geom_smooth(aes(BestSalinity, nTA), linetype=1, method = "lm")
    
  #geom_point(aes (BestSalinity, n35TA, fill=Season), alpha=0.5, shape=25)+
  #geom_smooth(aes(BestSalinity, n35TA), linetype=3, method = "lm")+  

#nTA_Sal
#ggsave(file="Outputs/Figure_1_Experiment_design.svg", plot=Design, dpi = 300, width=6, height=4)

DIC parameters to nDIC

kable(as.data.frame(DIC_coef[,1:8], format = "markdown"), 
      caption = " DIC parameters for normalization", digits = 3)

DIC parameters for normalization
Region	term	estimate	std.error	statistic	p.value	r.squared	adj.r.squared
FL	(Intercept)	1786.677	115.049	15.530	0.000	0.025	0.02
FL	BestSalinity	7.244	3.199	2.264	0.025	0.025	0.02

#write.csv(DIC_coef, "DIC_coef.csv")

nDIC_Sal<- ggplot(FLK.data) + MyTheme+
  facet_grid(~Zone)+
#nDIC_Sal<-ggplot(FLK.data[FLK.data$Zone=="Oceanic",] ) + theme_bw() +
  geom_point(aes (BestSalinity, DIC_umol_kg, fill=factor(Season)),
             alpha=0.5, shape=21)+
  geom_smooth(aes(BestSalinity, DIC_umol_kg),
              linetype=2, method = "lm", color="black")+
    
  geom_point(aes (BestSalinity, nDIC, fill=factor(Season)), alpha=0.5, shape=24)+
  geom_smooth(aes(BestSalinity, nDIC), linetype=1, method = "lm")
#nDIC_Sal

#ggsave(file="Outputs/Figure_1_Experiment_design.svg", plot=Design, dpi = 300, width=6, height=4)

FLK_parameters<-ggarrange(nTA_Sal, nDIC_Sal,
          #labels = c("DIC Friss regresion", "TA Friss regresion"),
          ncol = 2, nrow = 1)

FLK_parameters

Figure 1: Overview of linear regressions of total alkalinity (TA, µmol kg−1) and disolved inorganic carbon (DIC, µmol kg−1) as a function of salinity + Friss normalized values.

Original data: Black line and circles
Friis normalization: blue line and triangles
Highest and lowest salinities are mostlikely wrong

Bias of hour of the day per sample (EST)

All_ToD<- ggplot(FLK.data) + 
  
  theme_bw() +
  scale_y_continuous(limits = c(0, 24),
                      expand = c(0.01, 0.01),
                      breaks = seq(0, 24, 2),
                      name="Time of the day")+
  
  theme(legend.position="bottom",
          plot.background=element_blank(),
          panel.grid = element_blank(),
          legend.box.background = element_rect(),
          panel.background =element_rect(fill = NA, 
                                         color = "black")
          )

nTA_dat<-All_ToD +geom_point(aes(nTA, ToD, colour=Sub_region))#+
  #geom_smooth(aes(nDIC, ToD, colour=Sub_region), se=F)
ggMarginal(nTA_dat, groupColour = TRUE)

nDIC_dat<-All_ToD +geom_point(aes(nDIC, ToD, colour=Sub_region))#+
  #geom_smooth(aes(nDIC, ToD, colour=Sub_region), se=F)
ggMarginal(nDIC_dat, groupColour = TRUE)

omega_dat<-All_ToD +geom_point(aes(Aragonite_Sat_W, ToD, colour=Sub_region))#+
  #geom_smooth(aes(ToD, Aragonite_Sat_W, colour=Sub_region), se=F)
ggMarginal(omega_dat, groupColour = TRUE)

4. nTA and nDIC relation

4.1 Region (all FLK samples)

nTA_nDIC<- ggplot(FLK.data, aes (nDIC, nTA)) + 
  
  scale_y_continuous(#limits = c(1700,2900),
                     #expand = c(0, 0),
                     #breaks = seq(1800, 2800, 100),
                    name="nTA") +
  scale_x_continuous(# limits = c(1600, 2600),
                     # expand = c(0, 0),
                     # breaks = seq(1500,2600,100),
                      name="nDIC")+
MyTheme

#TA_DIC+ 
 #geom_point(aes (fill=Region, shape=Season), alpha=0.5)+
 #scale_shape_manual(values=c(21, 22, 23, 24))

#ggsave(file="Outputs/Figure_1_Experiment_design.svg", plot=Design, dpi = 300, width=6, height=4)

nTA_nDIC_sea<-nTA_nDIC +
   scale_shape_manual(values=c(21,21,24, 24))+
  geom_point(aes (fill=Season, shape=Zone), alpha=0.4)+
  geom_smooth(method = "lm", colour="black")

nTA_nDIC_pre<-nTA_nDIC +
  scale_shape_manual(values=c(21,21,24, 24))+
  geom_point(aes (fill=Precipitation,  shape=Zone), alpha=0.4)+
  geom_smooth(method = "lm", colour="black")

nTA_nDIC_parameters<-ggarrange(nTA_nDIC_sea, nTA_nDIC_pre,
          #labels = c("DIC Friss regresion", "TA Friss regresion"),
          ncol = 2, nrow = 1)

nTA_nDIC_parameters

Figure 2: Linear regressions of total alkalinity (nTA) as a function of disolved inorganic carbon (nDIC) for the Florida Keys .

Does this mean anything without offshore / inshore references?

 # Individual LR for each region
    TA_DIC_lm <- FLK.data %>%
      group_by(Region) %>%
      do(mod = lm(nTA ~ nDIC, data = .))

    TA_DIC_coef <- FLK.data %>% 
                    group_by(Region) %>%
                    do({model = lm(nTA~nDIC, data=.)  
                    # create model
                    data.frame(tidy(model), # get coefficient info
                    glance(model))})        # get model info

    kable(as.data.frame(TA_DIC_coef[,1:8], format = "markdown"),
          caption = "nTA vs nDIC equations", digits = 3)

nTA vs nDIC equations
Region	term	estimate	std.error	statistic	p.value	r.squared	adj.r.squared
FL	(Intercept)	799.496	23.601	33.876	0	0.74	0.739
FL	nDIC	0.769	0.012	66.390	0	0.74	0.739

4.2 By zone sub_region

nTA_nDIC_sea_zone<-nTA_nDIC_sea + facet_grid(Zone~Sub_region)
nTA_nDIC_sea_zone

nTA_nDIC_pre_zone<-nTA_nDIC_pre +  facet_grid(Zone~Sub_region)
nTA_nDIC_pre_zone

 # Individual LR for each Zone and Sub_region
    TA_DIC_lm_zone <- FLK.data %>%
      group_by(Zone, Sub_region) %>%
      do(mod = lm(nTA ~ nDIC, data = .))

    TA_DIC_coef_zone <- FLK.data %>% 
                    group_by(Zone, Sub_region) %>%
                    do({model = lm(nTA~nDIC, data=.)  
                    # create model
                    data.frame(tidy(model), # get coefficient info
                    glance(model))})        # get model info

    kable(as.data.frame(TA_DIC_coef_zone[,1:8], format = "markdown"),
          caption = "nTA vs nDIC equations", digits = 3)

nTA vs nDIC equations
Zone	Sub_region	term	estimate	std.error	statistic	p.value	r.squared
Inshore	BB	(Intercept)	1059.427	82.753	12.802	0.000	0.755
Inshore	BB	nDIC	0.639	0.040	15.903	0.000	0.755
Inshore	UK	(Intercept)	1194.043	61.452	19.430	0.000	0.782
Inshore	UK	nDIC	0.581	0.030	19.326	0.000	0.782
Inshore	MK	(Intercept)	638.217	67.189	9.499	0.000	0.837
Inshore	MK	nDIC	0.849	0.033	25.456	0.000	0.837
Inshore	LK	(Intercept)	611.538	77.404	7.901	0.000	0.782
Inshore	LK	nDIC	0.845	0.039	21.819	0.000	0.782
Mid channel	BB	(Intercept)	1463.099	99.429	14.715	0.000	0.534
Mid channel	BB	nDIC	0.446	0.048	9.203	0.000	0.534
Mid channel	UK	(Intercept)	1586.318	124.201	12.772	0.000	0.373
Mid channel	UK	nDIC	0.389	0.061	6.413	0.000	0.373
Mid channel	MK	(Intercept)	776.587	86.377	8.991	0.000	0.728
Mid channel	MK	nDIC	0.782	0.043	18.353	0.000	0.728
Mid channel	LK	(Intercept)	652.916	79.369	8.226	0.000	0.774
Mid channel	LK	nDIC	0.837	0.039	21.448	0.000	0.774
Offshore	BB	(Intercept)	1468.315	140.293	10.466	0.000	0.343
Offshore	BB	nDIC	0.445	0.068	6.503	0.000	0.343
Offshore	UK	(Intercept)	1585.265	99.772	15.889	0.000	0.372
Offshore	UK	nDIC	0.388	0.049	7.963	0.000	0.372
Offshore	MK	(Intercept)	1101.631	99.092	11.117	0.000	0.565
Offshore	MK	nDIC	0.624	0.049	12.848	0.000	0.565
Offshore	LK	(Intercept)	823.845	91.740	8.980	0.000	0.636
Offshore	LK	nDIC	0.758	0.045	16.930	0.000	0.636
Oceanic	UK	(Intercept)	1947.801	176.504	11.035	0.000	0.151
Oceanic	UK	nDIC	0.213	0.086	2.462	0.019	0.151
Oceanic	MK	(Intercept)	2232.766	101.776	21.938	0.000	0.026
Oceanic	MK	nDIC	0.073	0.050	1.461	0.148	0.026
Oceanic	LK	(Intercept)	1900.492	122.197	15.553	0.000	0.158
Oceanic	LK	nDIC	0.235	0.060	3.945	0.000	0.158

Notes:

Higher slopes in TA vs. DIC
- -> a higher NCC:NCP
- -> calcifying (e.g., corals, sediments, and calcifying algae) relative to non-calcifying communities (e.g., fleshy algae)
Intermediate slopes for mixed communities of calcifiers and non-calcifiers (Anthony et al., 2013; Page et al., 2016; Lantz et al., 2017).

4.3 nTA nDIC outliers

nTA_nDIC_Extreme<- ggplot() + 
    scale_y_continuous(#limits = c(1700,2900),
                     #expand = c(0, 0),
                     #breaks = seq(1800, 2800, 100),
                    name="nTA") +
  scale_x_continuous(# limits = c(1600, 2600),
                     # expand = c(0, 0),
                     # breaks = seq(1500,2600,100),
                      name="nDIC")+
  MyTheme +  facet_grid(Zone~Sub_region)+
  geom_point(data=FLK.data, aes (nDIC, nTA, fill=Extreme), shape=21, alpha=0.8)+
  geom_smooth(data=FLK.data[FLK.data$Extreme=="Normal", ], 
              aes (nDIC, nTA), method = "lm", colour="black")
nTA_nDIC_Extreme

 # Individual LR for each Zone and Sub_region
    
    TA_DIC_coef_zone_noExtremes <- FLK.data[FLK.data$Extreme=="Normal", ] %>% 
                    group_by(Zone, Sub_region) %>%
                    do({model = lm(nTA~nDIC, data=.)  
                    # create model
                    data.frame(tidy(model), # get coefficient info
                    glance(model))})        # get model info

    kable(as.data.frame(TA_DIC_coef_zone_noExtremes[,1:8], format = "markdown"),
          caption = "nTA vs nDIC equations without extreme events", digits = 3)

nTA vs nDIC equations without extreme events
Zone	Sub_region	term	estimate	std.error	statistic	p.value	r.squared
Inshore	BB	(Intercept)	1035.503	82.386	12.569	0.000	0.770
Inshore	BB	nDIC	0.650	0.040	16.248	0.000	0.770
Inshore	UK	(Intercept)	1199.056	59.144	20.274	0.000	0.801
Inshore	UK	nDIC	0.578	0.029	19.978	0.000	0.801
Inshore	MK	(Intercept)	683.173	64.184	10.644	0.000	0.850
Inshore	MK	nDIC	0.826	0.032	25.928	0.000	0.850
Inshore	LK	(Intercept)	605.335	78.412	7.720	0.000	0.790
Inshore	LK	nDIC	0.848	0.039	21.618	0.000	0.790
Mid channel	BB	(Intercept)	1398.325	96.438	14.500	0.000	0.586
Mid channel	BB	nDIC	0.477	0.047	10.158	0.000	0.586
Mid channel	UK	(Intercept)	1600.452	123.694	12.939	0.000	0.370
Mid channel	UK	nDIC	0.382	0.060	6.320	0.000	0.370
Mid channel	MK	(Intercept)	754.533	73.735	10.233	0.000	0.800
Mid channel	MK	nDIC	0.793	0.036	21.817	0.000	0.800
Mid channel	LK	(Intercept)	754.430	74.022	10.192	0.000	0.787
Mid channel	LK	nDIC	0.787	0.036	21.601	0.000	0.787
Offshore	BB	(Intercept)	1316.540	135.042	9.749	0.000	0.443
Offshore	BB	nDIC	0.518	0.066	7.872	0.000	0.443
Offshore	UK	(Intercept)	1522.502	96.029	15.855	0.000	0.431
Offshore	UK	nDIC	0.418	0.047	8.922	0.000	0.431
Offshore	MK	(Intercept)	964.263	78.232	12.326	0.000	0.730
Offshore	MK	nDIC	0.691	0.038	18.023	0.000	0.730
Offshore	LK	(Intercept)	1354.983	81.157	16.696	0.000	0.503
Offshore	LK	nDIC	0.498	0.040	12.571	0.000	0.503
Oceanic	UK	(Intercept)	1739.983	151.875	11.457	0.000	0.358
Oceanic	UK	nDIC	0.313	0.074	4.220	0.000	0.358
Oceanic	MK	(Intercept)	2157.122	102.764	20.991	0.000	0.059
Oceanic	MK	nDIC	0.109	0.050	2.177	0.033	0.059
Oceanic	LK	(Intercept)	1646.591	99.873	16.487	0.000	0.413
Oceanic	LK	nDIC	0.358	0.049	7.353	0.000	0.413

Notes: * Offshore high nTA and nDIC during march 2012 cold mortality event - keep or remove?

5. Basic stats summary and visual

5.1 Temperature

FLK.data$Date<-FLK.data$ESTDate

Season stats

Temperature by season
Season	Zone	Tmin	Tmax	Tmean	Tsd
Fall	Inshore	20.01500	30.02900	25.59673	2.5421481
Fall	Mid channel	21.98100	29.85110	26.62821	2.0493446
Fall	Offshore	24.87700	29.97035	27.20738	1.5786848
Fall	Oceanic	24.90300	30.01320	27.39854	1.5239508
Spring	Inshore	22.23800	32.79000	27.85472	1.9082703
Spring	Mid channel	24.46990	31.26000	27.59277	1.6558646
Spring	Offshore	24.72433	31.12400	27.20822	1.4543694
Spring	Oceanic	25.07167	30.70000	27.28511	1.4048604
Summer	Inshore	26.85130	33.41420	30.94559	1.3603574
Summer	Mid channel	27.69640	33.54900	30.66481	1.0530289
Summer	Offshore	28.57000	32.47400	30.35070	0.7031212
Summer	Oceanic	29.36600	31.42030	30.27019	0.4979293
Winter	Inshore	17.50500	26.65400	22.66720	2.3468100
Winter	Mid channel	17.91567	26.79900	23.63902	1.8934145
Winter	Offshore	19.19967	27.21600	24.27852	1.5846746
Winter	Oceanic	21.34400	27.30000	24.58368	1.6286770

Overview

Monthly

5.2 Salinity

Season stats

Salinity by season
Season	Zone	Sal_min	Sal_max	Sal_mean	Sal_sd
Fall	Inshore	30.45233	38.11319	35.35753	1.2447767
Fall	Mid channel	31.88800	37.26015	35.62053	0.8406938
Fall	Offshore	33.47100	36.86900	35.71652	0.6347185
Fall	Oceanic	33.90255	37.18100	35.78349	0.5204920
Spring	Inshore	35.08521	37.73900	36.67620	0.5579413
Spring	Mid channel	26.95708	37.23920	36.34520	0.9946257
Spring	Offshore	34.82339	36.73600	36.33263	0.2553492
Spring	Oceanic	35.75627	36.66305	36.31844	0.2095783
Summer	Inshore	32.05500	41.03199	36.17870	1.1432207
Summer	Mid channel	33.94800	36.86176	35.86870	0.6988476
Summer	Offshore	33.84100	36.55435	35.72740	0.6229552
Summer	Oceanic	32.73250	36.51863	35.56385	0.7789012
Winter	Inshore	31.23138	38.32563	35.98450	0.9184624
Winter	Mid channel	33.25478	36.77829	36.12218	0.5298124
Winter	Offshore	33.64836	36.62865	36.20181	0.4361290
Winter	Oceanic	35.47867	36.56613	36.23997	0.2947979

Overview

Monthly

Salinity issues

Salinity_low<-FLK.data[FLK.data$BestSalinity<30, ]
Salinity_high<-FLK.data[FLK.data$BestSalinity>40, ]
Salinity_Check<-rbind(Salinity_low, Salinity_high)
Salinity_Check<-Salinity_Check[!is.na(Salinity_Check$BestSalinity),]
Salinity_Check$Problem<-"salinity"
Salinity_Check

Salinity - Temperatue correlations and outliers

5.3 Aragonite

Season stats

Omega by season
Season	Zone	Amin	Amax	Amean	Asd
Fall	Inshore	2.265938	4.153585	3.237630	0.3377839
Fall	Mid channel	2.425071	4.194368	3.554209	0.2828936
Fall	Offshore	3.172859	4.472063	3.722611	0.2317087
Fall	Oceanic	3.261485	4.216995	3.779979	0.2274992
Spring	Inshore	2.804235	5.083392	4.139778	0.4495737
Spring	Mid channel	2.766048	4.711981	3.965309	0.3038914
Spring	Offshore	2.999385	4.251338	3.780495	0.1547279
Spring	Oceanic	1.998959	4.078478	3.764759	0.2894793
Summer	Inshore	1.835257	5.148959	3.621862	0.5640274
Summer	Mid channel	2.808160	4.918125	3.803552	0.3656937
Summer	Offshore	3.110143	5.133387	3.905004	0.3108872
Summer	Oceanic	3.553370	4.945832	4.016958	0.3165640
Winter	Inshore	2.401692	4.886632	3.626888	0.4247764
Winter	Mid channel	2.339249	4.649625	3.624081	0.3020022
Winter	Offshore	3.201501	4.312818	3.651986	0.1540584
Winter	Oceanic	2.491916	3.996918	3.676481	0.2192837

Overview

Monthly

Omega issues

Omega_low<-FLK.data[FLK.data$Aragonite_Sat_W<2.1, ]
Omega_low2<-FLK.data[FLK.data$Aragonite_Sat_W<3 & FLK.data$Zone=="Oceanic", ]
Omega_high<-FLK.data[FLK.data$Extreme=="HighOmega_NoReason", ]
Omega_Check<-rbind(Omega_low, Omega_low2, Omega_high)
Omega_Check<-Omega_Check[!is.na(Omega_Check$Aragonite_Sat_W),]
Omega_Check$Problem<-"omega"
Omega_Check

Omega - Temperature correlation and outliers

Omega - Salinity correlation and outliers

5.4 TA

Season stats

TA by season
Season	Zone	TAmin	TAmax	TAmean	TAsd
Fall	Inshore	2099.544	2609.396	2371.411	87.47261
Fall	Mid channel	2174.910	2479.733	2372.264	43.14992
Fall	Offshore	2245.810	2406.123	2376.256	20.65044
Fall	Oceanic	2353.810	2406.854	2379.771	10.49261
Spring	Inshore	2091.859	2398.144	2298.375	71.80582
Spring	Mid channel	2114.163	2406.498	2353.235	52.62884
Spring	Offshore	2262.590	2426.836	2376.541	21.89812
Spring	Oceanic	2339.600	2403.372	2382.003	12.16319
Summer	Inshore	1943.671	2494.752	2280.082	100.46661
Summer	Mid channel	2131.950	2411.730	2338.454	57.52611
Summer	Offshore	2293.290	2422.590	2369.555	25.84041
Summer	Oceanic	2343.852	2424.110	2382.153	16.92446
Winter	Inshore	2251.164	2781.489	2430.625	85.09654
Winter	Mid channel	2274.057	2678.780	2401.440	50.82598
Winter	Offshore	2352.434	2722.780	2389.688	36.50677
Winter	Oceanic	2354.310	2420.980	2383.905	15.82864

nTA by season
Season	Zone	TAmin	TAmax	TAmean	TAsd
Fall	Inshore	2100.064	2622.140	2375.285	88.85852
Fall	Mid channel	2183.064	2467.205	2374.212	41.57793
Fall	Offshore	2255.923	2426.815	2378.096	19.89557
Fall	Oceanic	2354.059	2413.604	2381.123	10.71523
Spring	Inshore	2092.180	2394.755	2295.018	71.13007
Spring	Mid channel	2114.174	2505.353	2351.298	54.11024
Spring	Offshore	2262.552	2426.357	2374.064	21.65720
Spring	Oceanic	2340.002	2399.029	2379.553	11.49093
Summer	Inshore	1945.293	2491.039	2280.554	101.12065
Summer	Mid channel	2131.469	2409.119	2339.561	58.29176
Summer	Offshore	2290.618	2423.919	2371.507	25.92518
Summer	Oceanic	2352.364	2430.939	2385.340	14.82441
Winter	Inshore	2251.383	2799.119	2431.724	89.96143
Winter	Mid channel	2287.434	2686.268	2400.438	51.33171
Winter	Offshore	2351.490	2719.391	2388.182	36.61054
Winter	Oceanic	2354.871	2416.189	2382.046	15.59239

Overview - outliers

Monthly

nTA issues

TA_low<-FLK.data[FLK.data$nTA<2000, ] # One sample (WS20231_26) low TA no DIC
TA_high<-FLK.data[FLK.data$nTA>2780, ] # One sample (WS17030_16) high
TA_Check<-rbind(TA_low, TA_high)
TA_Check<-TA_Check[!is.na(TA_Check$nTA),]
TA_Check$Problem<-"TA"
TA_Check

Omega- nTA scatterplot and outliers

5.5 DIC

Season stats

DIC by season
Season	Zone	DIC_min	DIC_max	DIC_mean	DIC_sd
Fall	Inshore	1834.164	2347.652	2088.697	84.08436
Fall	Mid channel	1940.415	2188.637	2060.520	40.73712
Fall	Offshore	1956.640	2113.588	2049.048	29.11836
Fall	Oceanic	1957.930	2118.330	2047.986	29.49927
Spring	Inshore	1724.374	2078.210	1923.199	72.07535
Spring	Mid channel	1793.125	2109.220	2000.008	54.45072
Spring	Offshore	1918.379	2118.600	2042.020	24.54888
Spring	Oceanic	2003.835	2182.680	2049.730	24.77367
Summer	Inshore	1704.388	2186.988	1970.661	94.51700
Summer	Mid channel	1783.686	2159.260	2009.134	59.21836
Summer	Offshore	1925.061	2108.781	2032.781	32.27661
Summer	Oceanic	1979.375	2102.520	2036.585	28.68785
Winter	Inshore	1943.358	2429.507	2106.100	87.95805
Winter	Mid channel	1991.463	2310.755	2076.757	53.87955
Winter	Offshore	2012.429	2339.885	2062.173	33.69393
Winter	Oceanic	2016.510	2158.478	2054.552	23.70995

nDIC by season
Season	Zone	nDIC_min	nDIC_max	nDIC_mean	nDIC_sd
Fall	Inshore	1832.106	2362.012	2094.173	85.90610
Fall	Mid channel	1954.981	2178.163	2063.056	39.12974
Fall	Offshore	1964.187	2115.286	2050.979	28.34617
Fall	Oceanic	1962.486	2130.510	2049.550	29.94421
Spring	Inshore	1723.896	2080.748	1921.029	71.79103
Spring	Mid channel	1793.153	2115.160	1998.366	55.47389
Spring	Offshore	1918.345	2117.455	2039.639	24.37869
Spring	Oceanic	2002.461	2183.378	2047.379	24.80366
Summer	Inshore	1710.812	2197.289	1971.580	96.76952
Summer	Mid channel	1783.700	2171.288	2010.568	61.56348
Summer	Offshore	1923.027	2114.534	2034.798	33.28274
Summer	Oceanic	1980.710	2103.650	2039.790	28.78456
Winter	Inshore	1938.572	2446.424	2107.280	92.31747
Winter	Mid channel	1988.273	2317.674	2075.972	55.14798
Winter	Offshore	2010.643	2336.817	2060.678	34.17997
Winter	Oceanic	2017.041	2158.632	2052.765	24.04813

Overview - outliers

Monthly

nDIC issues

Not filtering additional data becuase of nDIC

Omega - nDIC scatterplot and outliers

NOTE: Review Omega calculations for 2011 - Very high values for normal DIC and TA - including offshore and ocean samples

5.6 pH

Season stats

pH by season
Season	Zone	pH_min	pH_max	pH_mean	pH_sd
Fall	Inshore	7.840293	8.109975	7.988004	0.0618658
Fall	Mid channel	7.908322	8.092337	8.019632	0.0406883
Fall	Offshore	7.937204	8.157007	8.034243	0.0379669
Fall	Oceanic	7.949192	8.104629	8.038466	0.0386797
Spring	Inshore	7.875770	8.271259	8.099446	0.0769675
Spring	Mid channel	7.877835	8.221791	8.066494	0.0547752
Spring	Offshore	7.942578	8.100547	8.040308	0.0286746
Spring	Oceanic	7.730269	8.092541	8.033934	0.0495861
Summer	Inshore	7.672562	8.172603	7.966685	0.0765092
Summer	Mid channel	7.868227	8.125938	7.990184	0.0457906
Summer	Offshore	7.883689	8.147040	8.004235	0.0413498
Summer	Oceanic	7.958271	8.119377	8.020132	0.0414338
Winter	Inshore	7.925370	8.246944	8.087370	0.0623184
Winter	Mid channel	7.920679	8.230011	8.076427	0.0439480
Winter	Offshore	8.033234	8.183797	8.073114	0.0242499
Winter	Oceanic	7.902464	8.144928	8.071353	0.0348490

Overview - outliers

Monthly

pH issues

pH_low<-FLK.data[FLK.data$pH_calculated<7.95 & FLK.data$Zone=="Oceanic", ]
pH_low$Problem<-"pH(low_for_ocean)"

Omega - nDIC scatterplot and outliers

NOTE: Review Omega calculations for August 2011 - Very high values for normal DIC and TA - including offshore and ocean samples

6 Remove outliers

Remove<-rbind(Salinity_Check, Omega_Check, TA_Check, pH_low)
Remove <-Remove %>% 
        filter(!duplicated(CTDID))

#write.csv(Remove, "FLK_results/ValuesToRemove_V3.csv", row.names = F)

FLK.data_filtered<-FLK.data[!(FLK.data$CTDID %in% Remove$CTDID),]
FLK.data_filtered<-FLK.data_filtered%>% select (- (ncrmp_flag: seacarb_id))
#str(FLK.data_filtered)
#write.csv(FLK.data_filtered, "FLK_results/FLK_filtered_ve3.csv", row.names = F)

7. Notes/ideas

Regional CC -> offshore data
Local carbonate chemistry:
- Seasonal (light), storms, upwelling, salinity (rain).
- Biological: coral/seagrass/macroalgae cover
- Biological + Seasonal: Photosyntesis, respiration.
- Physical: inlets, rivers, upwelling

Manzello (2012)

local carbonate chemistry: Variation on CO2, alkalinity, and salinity - Florida Reef Tract (FRT) data.
Meassure total CO2 (TCO2) - coulometrically and total alkalinity (TA) - gran titration. Other paramethers derived
Predominantly in inshore / upper FRT (higher than modeled).
H Productivity -> net uptake of total CO2 (TCO2) -> H aragonite *Offshore reefs: oceanic carbonate chemistries consistent with present day tropical surface ocean conditions.
Photosynthetic uptake of TCO2 by seagrasses/macroalgae in the inshore waters of the FRT. Acidification refugia (spatial * time)

Uthicke (2014)

GBR - 14 Inshore fringing reefs Meassured: TA and DIC. Derived:pCO2, pH and aragonite
TA: 2069 - 2364 mmol kg2
DIC:1846 - 2099 mmol kg2
pCO2: 340 - 554 matm
- Higher wet seasons
- Inshore reefs above atmospheric values.
- Arg was not further reduced in the wet season (temp effects) *Thermodynamic effects and anthropogenic runoff (P, R and P/R ratios).
- Expected slope of linear regression bw DIC and TA in systems where calcification is dominating is ~ 2.0

Cyronak (2018)

Net community production; NCP - organic
net community calcification; NCC inorganic
23 coral reef locations across the globe - derived from TA and DIC
All reefs were net calcifying for the majority of observations (alkalinity depletion relative to offshore)
Occasional observations of net dissolution occurred at most locations
Influence of organic carbon fluxes on total changes in dissolved inorganic carbon (DIC) (i.e., NCP compared to the sum of NCP and NCC) = 32% to 88% -> biogeochemical differences between reefs.
Reefs with the largest relative percentage of NCP experienced the largest variability in seawater pH for a given change in DIC -> ability to elevate or suppress local pH relative to the open ocean.

Ian (2019)

Reefs and inlets were sampled
Reefs: Elevated pCO2 in warmer wet season
Inlets: more dynamic -> periodic pulses of acidified water -> more acidification than reefs.
- Negative correlation bw salinity and both TA, and DIC. Elevated TA and DIC in low salinity waters -> carbonate dissolution as a result of organic matter decomposition?.

Sub_region	2010	2011	2012	2014	2015	2016	2017	2018	2019	2020	2021
BB	4	14	3	6	30	36	30	35	24	24	37
UK	7	19	3	8	45	51	42	45	30	31	45
MK	15	51	9	11	55	66	55	56	44	44	65
LK	18	61	9	11	58	72	60	76	53	46	70

Sub_region	2010	2011	2012	2014	2015	2016	2017	2018	2019	2020	2021
BB	4	14	3	6	30	36	30	35	24	24	37
UK	7	19	3	8	45	51	42	45	30	31	45
MK	15	51	9	11	55	66	55	56	44	44	65
LK	18	61	9	11	58	72	60	76	53	46	70

Carbon Chemestry explore - New database

Ana Palacio-Castro

2022-02-23

Libraries and settings

1. Import and format carbon chemestry data

Add seasonality

Subset only FK data

Label known extreme events to check outliers

2. Check data available

2.1 Locations (all samples)

2.2 Average GPS point location

2.3 Number of samples

By zone and location

By year and sub_region

By climatological season and region

By Month-year

By Month-region

Per station during all sampling time

3. TA and DIC salinity normalization

Salinity standarizations

Bias of hour of the day per sample (EST)

4. nTA and nDIC relation

4.1 Region (all FLK samples)

4.2 By zone sub_region

4.3 nTA nDIC outliers

5. Basic stats summary and visual

5.1 Temperature

Season stats

Overview

Monthly

5.2 Salinity

Season stats

Overview

Monthly

Salinity issues

Salinity - Temperatue correlations and outliers

5.3 Aragonite

Season stats

Overview

Monthly

Omega issues

Omega - Temperature correlation and outliers

Omega - Salinity correlation and outliers

5.4 TA

Season stats

Overview - outliers

Monthly

nTA issues

Omega- nTA scatterplot and outliers

5.5 DIC

Season stats

Overview - outliers

Monthly

nDIC issues

Omega - nDIC scatterplot and outliers

5.6 pH

Season stats

Overview - outliers

Monthly

pH issues

Omega - nDIC scatterplot and outliers

6 Remove outliers

7. Notes/ideas

Manzello (2012)

Uthicke (2014)

Cyronak (2018)

Ian (2019)

Sub_region	2010	2011	2012	2014	2015	2016	2017	2018	2019	2020	2021
BB	4	14	3	6	30	36	30	35	24	24	37
UK	7	19	3	8	45	51	42	45	30	31	45
MK	15	51	9	11	55	66	55	56	44	44	65
LK	18	61	9	11	58	72	60	76	53	46	70